DocumentCode
13682
Title
Cross-community sensing and mining
Author
Bin Guo ; Zhiwen Yu ; Daqing Zhang ; Xingshe Zhou
Author_Institution
Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi´an, China
Volume
52
Issue
8
fYear
2014
fDate
Aug. 2014
Firstpage
144
Lastpage
152
Abstract
With the developments in information and communications technology (ICT), people are involving in and connecting via various forms of communities in the cyber-physical space, such as online communities, opportunistic (offline) social networks, and location-based social networks. Different communities have distinct features and strengths. With humans playing the bridge role, these communities are implicitly interlinked. In contrast with the existing studies that mostly consider a single community, this article addresses the interaction among distinct communities. In particular, we present an emerging research area - cross-community sensing and mining (CSM), which aims to connect heterogeneous, cross-space communities by revealing the complex linkage and interplay among their properties and identifying human behavior patterns by analyzing the data sensed/collected from multi-community environments. The article describes and discusses the research background, characters, general framework, research challenges, as well as our practice of CSM.
Keywords
data mining; social networking (online); CSM; complex linkage; cross-community sensing and mining; cross-space communities; human behavior pattern identification; Cellular phones; Data mining; Information technology; Internet; Mobile communication; Social network services;
fLanguage
English
Journal_Title
Communications Magazine, IEEE
Publisher
ieee
ISSN
0163-6804
Type
jour
DOI
10.1109/MCOM.2014.6871682
Filename
6871682
Link To Document